In the fields of digital radio communication, packet communication represented by Internet communication or speech storage or the like, speech signal encoding/decoding techniques are indispensable for realizing effective use of transmission path capacity and storage media for radio wave or the like. Among those techniques, the speech encoding/decoding technique according to a CELP (Code Excited Linear Prediction) scheme constitutes a mainstream technique.
A CELP-based speech encoding apparatus encodes input speech based on a prestored speech model. To be more specific, the CELP-based speech encoding apparatus divides a digitized speech signal into frames of a fixed time interval on the order of 10 to 20 ms, performs linear predictive analysis on a speech signal in each frame, calculates a linear prediction coefficient (LPC) and a linear predictive residual vector and encodes the linear prediction coefficient and the linear predictive residual vector individually. As a method of encoding a linear prediction coefficient, it is a general practice to convert the linear prediction coefficient to an LSP (Line Spectral Pairs) parameter and encode the LSP parameter. Furthermore, as a method of encoding the LSP parameter, it is a frequently used practice to subject the LSP parameter to vector quantization. Vector quantization is a method whereby a code vector closest to a vector to be quantized is selected from a codebook having a plurality of representative vectors and an index (code) assigned to the selected code vector is outputted as a quantization result. In vector quantization, the codebook size is determined according to the amount of information available. When, for example, vector quantization is performed with the amount of information of 8 bits, a codebook can be configured using 256 (=28) types of code vectors.
Furthermore, to reduce the amount of information and the amount of calculation in vector quantization, various techniques are used such as Multi-stage Vector Quantization (MSVQ), Split Vector Quantization (SVQ) (e.g. see Non-Patent Literature 1). Multi-stage vector quantization is a method of vector-quantizing a vector once and further vector-quantizing a quantization error, and split vector quantization is a method of dividing a vector into a plurality of portions and quantizing the respective split vectors obtained.
Furthermore, classified vector quantization (classified VQ) is available as a technique of switching between codebooks used for vector quantization as appropriate according to phonetic characteristics (e.g. information of voicing, devoicing and mode of speech or the like) having a correlation with LSP to be quantized, thereby performing vector quantization appropriate for LSP characteristics and further improving performance of LSP encoding (e.g. see Non-Patent Literature 2). For example, using a cross correlation between a wide-band LSP (LSP obtained from a wide-band signal) and a narrow-band LSP (LSP obtained from a narrow-band signal), scalable encoding classifies the narrow-band LSP according to its characteristics, switches the first-stage codebook in multi-stage vector quantization according to the type of narrow-band LSP characteristics (hereinafter abbreviated as “type of narrow-band LSP”) and vector-quantizes the wide-band LSP.
Furthermore, studies are being carried out on performing vector quantization by combining multi-stage vector quantization and classified vector quantization. In this case, the quantization performance can be improved by providing a plurality of codebook groups (first-stage codebook, second-stage codebook, . . . ) made up of a plurality of stages of multi-stage vector quantization according to the type of the narrow-band LSP, whereas a plurality of codebook groups need to be provided and a greater memory is thereby required. Thus, studies are being carried out on the possibility, when performing vector quantization by combining multi-stage vector quantization and classified vector quantization, that only the first-stage codebook may be switched according to the type of the narrow-band LSP and common codebooks from the second stage onward are used for all types of the narrow-band LSP (e.g. see Patent Literature 1). In multi-stage vector quantization, this makes it possible to prevent the memory from increasing while obtaining the effect of improving quantization performance through classified vector quantization.